“After all, data that doesn’t do anything is just data; Data that drives action… is intelligence.”
It is important to understand the hype behind Big Data and the realities of how companies can utilize Big Data to create value. This initiative can be broken into three segments. One part of this blog series will be covering the different aspects of Business Intelligence. The second part will include concepts on how technology can be integrated and leveraged to create maximum value and operational excellence, but only with proper controls in place to insure quality and compliance is enforced. The third branch of this blog series is a realistic case study to show what this Enterprise Intelligence Solution can really look like in a practical sense and what type of return on investment one can expect, if implemented properly.
ROI and Enterprise Intelligence
The hype behind Big Data really is driven by the potential value that Big Data can provide for organizations. The basic premise is that with the appropriate enterprise intelligence strategy and appropriate complementary technologies, an intelligence solution can provide significant returns on investment. Organizations are interested in intelligence capabilities and how Big Data allows them to have a competitive edge or how it can drastically reduce cost of goods sold. It is also important to understand the different stages of data and how data can be leveraged within an enterprise for specific initiatives, especially those that promote and enable a continuous improvement culture. The danger and horror stories around poorly executed Big Data implementations are typically those that do not take a holistic approach to the solution. Instead, some organizations make the perilous mistake of focusing on just the data, or just the technology, or even just a specific application. Unfortunately (contrary to popular belief and application sales literature), there is not a single application on the market that can provide a complete end-to-end Business Intelligence (BI) Solution. An organization’s data is spread amongst many data sources with different contexts. In addition, the real power of data is lost if it is not capable of being used in applications for decision making.
Implementing an Intelligence Strategy
Real-time Enterprise, Big Data, Manufacturing Intelligence, and Enterprise Intelligence, along with a host of other “buzz” words are really attempting to solve the same problem. The real limitation with each of these is that if an organization does not first set out an Enterprise Level Intelligence Strategy, any solution will have minimal gains. Numerous variables impact the actual use of this data from its intuitiveness and structure all the way down to the method of delivery. Many times the delivery is often overlooked, however even the medium used to deliver the message will make a difference as to whether or not the data will be useful or not. An organization must understand that one of the main objectives of an Enterprise Intelligence Strategy is that you are taking a holistic approach to the solution and leveraging the strengths of current investments as well as introducing new systems or technologies that will maximize the value that your specific data can provide. In addition, it is also important to understand that contextualization is an integral part of the solution design process and should be incorporated into the overall strategy to insure the solution is also providing a single version of the truth. Included in the following whitepaper is a brief coverage of the specific limitations of an application-centric approach, why there is a need to first adopt a larger perspective before selecting your technology, and how to understand enterprise level intelligence at the macro level. The importance of incorporating manufacturing technologies to protect your current investments, provide advanced intelligence capabilities and mitigate risk (especially during improvement activities) will also be covered. Most importantly, this should enable one to understand how the business models themselves can be coupled with integrated platforms to be leveraged to drive, foresee and even automate actions.
Just to point out, these are revolutionary times. Some of the younger generation reading this text may not see that a revolution is happening, but we have the privilege of working with some of the most innovative companies in the world, primarily in the Life Sciences sector. Some of Seabrook’s clients are not just changing their organization with this data but they are rocking the entire industry. Some of them are not even changing the industry; they are changing the world. We have organizations that are enabling people to manage their illnesses better, or, even curing those illnesses. Some of our clients are providing genetic technology that allows us to analyze things at such a molecular level that we can actually grow nutrient-rich vegetation in the harshest climates. We are potentially talking about solving world hunger. Therefore, we see ourselves as privileged to work with these companies. This data is more than making a profit; it can literally enable us to change the world. There is not one pill you can take that will solve all of your organization’s problems. More and more, organizations are coming under tighter regulatory scrutiny. More advanced technologies are required to manufacture products and customers themselves are requiring better electronic products and more complicated pharmaceuticals. All of these requirements are creating challenges for the Big Data concept and the technologies that accompany it.
Look out for the next edition of this Blog Series – Enterprise Intelligence: What is it & Why do we need it.